Location of wireless nodes using signal strength weighting metric

ABSTRACT

Methods, apparatuses, and systems directed to a wireless node location mechanism that uses a signal strength weighting metric to improve the accuracy of estimating the location of a wireless node based on signals detected among a plurality of radio transceivers. In certain implementations, the wireless node location mechanism further incorporates a differential signal strength metric to reduce the errors caused by variations in wireless node transmit power, errors in signal strength detection, and/or direction-dependent path loss. As opposed to using the absolute signal strength or power of an RF signal transmitted by a wireless node, implementations of the present invention compare the differences between signal strength values detected at various pairs of radio receivers to corresponding differences characterized in a model of the RF environment. One implementation of the invention searches for the locations in the model between each pair of radio receivers where their signal strength is different by an observed amount.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

This application makes reference to the following commonly owned U.S.patent applications and/or patents, which are incorporated herein byreference in their entirety for all purposes:

U.S. patent application Ser. No. 10/155,938 in the name of Patrice R.Calhoun, Robert B. O'Hara, Jr. and Robert J. Friday, entitled “Methodand System for Hierarchical Processing of Protocol Information in aWireless LAN;”

U.S. application Ser. No. 10/183,704 in the name of Robert J. Friday,Patrice R. Calhoun, Robert B. O'Hara, Jr., Alexander H. Hills and PaulF. Dietrich, and entitled “Method and System for Dynamically AssigningChannels Across Multiple Radios in a Wireless LAN;”

U.S. patent application Ser. No. 10/407,357 in the name of Patrice R.Calhoun, Robert B. O'Hara, Jr. and Robert J. Friday, entitled “Methodand System for Hierarchical Processing of Protocol Information in aWireless LAN;”

U.S. patent application Ser. No. 10/407,370 in the name of Patrice R.Calhoun, Robert B. O'Hara, Jr. and David A. Frascone, entitled “WirelessNetwork System Including Integrated Rogue Access Point Detection;” and

U.S. application Ser. No. 10/447,735 in the name of Robert B. O'Hara,Jr., Robert J. Friday, Patrice R. Calhoun, and Paul F. Dietrich andentitled “Wireless Network Infrastructure including Wireless Discoveryand Communication Mechanism.”

FIELD OF THE INVENTION

The present invention relates to estimating the location of wirelessnodes in RF environments and, more particularly, to a signal strengthweighting metric directed to improving the accuracy of wireless nodelocation mechanisms.

BACKGROUND OF THE INVENTION

Market adoption of wireless LAN (WLAN) technology has exploded, as usersfrom a wide range of backgrounds and vertical industries have broughtthis technology into their homes, offices, and increasingly into thepublic air space. This inflection point has highlighted not only thelimitations of earlier-generation systems, but the changing role WLANtechnology now plays in people's work and lifestyles, across the globe.Indeed, WLANs are rapidly changing from convenience networks tobusiness-critical networks. Increasingly users are depending on WLANs toimprove the timeliness and productivity of their communications andapplications, and in doing so, require greater visibility, security,management, and performance from their network.

The rapid proliferation of lightweight, portable computing devices andhigh-speed WLANs enables users to remain connected to various networkresources, while roaming throughout a building or other physicallocation. The mobility afforded by WLANs has generated a lot of interestin applications and services that are a function of a mobile user'sphysical location. Examples of such applications include: printing adocument on the nearest printer, locating a mobile user or rogue accesspoint, displaying a map of the immediate surroundings, and guiding auser inside a building. The required or desired granularity of locationinformation varies from one application to another. Indeed, the accuracyrequired by an application that selects the nearest network printer, orlocates a rogue access point, often requires the ability to determine inwhat room a wireless node is located. Accordingly, much effort has beendedicated to improving the accuracy of wireless node locationmechanisms.

The use of radio signals to estimate the location of a wireless deviceor node is known. For example, a Global Positioning System (GPS)receiver obtains location information by triangulating its positionrelative to four satellites that transmit radio signals. The GPSreceiver estimates the distance between each satellite based on the timeit takes for the radio signals to travel from the satellite to thereceiver. Signal propagation time is assessed by determining the timeshift required to synchronize the pseudo-random signal transmitted bythe satellite and the signal received at the GPS receiver. Althoughtriangulation only requires distance measurements from three points, anadditional distance measurement from a fourth satellite is used forerror correction.

The distance between a wireless transmitter and a receiver can also beestimated based on the strength of the received signal, or moreaccurately the observed attenuation of the radio signal. Signalattenuation refers to the weakening of a signal over its path of traveldue to various factors like terrain, obstructions and environmentalconditions. Generally speaking, the magnitude or power of a radio signalweakens as it travels from its source. The attenuation undergone by anelectromagnetic wave in transit between a transmitter and a receiver isreferred to as path loss. Path loss may be due to many effects such asfree-space loss, refraction, reflection, and absorption.

In business enterprise environments, most location-tracking systems arebased on RF triangulation or RF fingerprinting techniques. RFtriangulation calculates a mobile user's location based upon thedetected signal strength of nearby access points (APs). It naturallyassumes that signal strength is a factor of proximity, which is true amajority of the time. However, the multipath phenomenon encountered inindoor RF environments does present certain difficulties in locatingwireless nodes, since reflection and absorption of RF signals affectsthe correlation between signal strength and proximity. RF fingerprintingcompares a mobile station's view of the network infrastructure (i.e.,the strength of signals transmitted by infrastructure access points)with a database that contains an RF physical model of the coverage area.This database is typically populated by either an extensive site surveyor an RF prediction model of the coverage area. For example, Bahl etal., “A Software System for Locating Mobile Users: Design, Evaluation,and Lessons,” http://research.microsoft.com/˜bahl/Papers/Pdf/radar.pdf,describes an RF location system (the RADAR system) in a WLANenvironment, that allows a mobile station to track its own locationrelative to access points in a WLAN environment.

The RADAR system relies on a so-called Radio Map, which is a database oflocations in a building and the signal strength of the beacon packetsemanating from the access points as observed, or estimated, at thoselocations. For example, an entry in the Radio Map may look like (x, y,z, ss_(i)(i=1 . . . n)), where (x, y, z) are the physical coordinates ofthe location where the signal is recorded, and ss_(i) is the signalstrength of the beacon signal emanating from the ith access point.According to Bahl et al., Radio Maps may be empirically created based onheuristic evaluations of the signals transmitted by the infrastructureradios at various locations, or mathematically created using amathematical model of indoor RF signal propagation. To locate theposition of the mobile user in real-time, the mobile station measuresthe signal strength of each of access points within range. It thensearches a Radio Map database against the detected signal strengths tofind the location with the best match. Bahl et al. also describeaveraging the detected signal strength samples, and using a trackinghistory-based algorithm, to improve the accuracy of the locationestimate. Bahl et al. also address fluctuations in RF signal propagationby using multiple Radio Maps and choosing the Radio Map which bestreflects the current RF environment. Specifically, one access pointdetects beacon packets from other access points and consults a radio mapto estimate its location, and evaluates the estimated location with theknown location. The RADAR system chooses the Radio Map which bestcharacterizes the current RF environment, based on a sliding windowaverage of received signal strengths.

While the RADAR system works for its intended objective, even in thissystem, location accuracy decreases with the error in detecting thestrength of RF signals. For example, the accuracy of signal strengthdetection between a radio transmitter and a radio receiver whose signalstrength measurements are used to estimate location, and thus, theaccuracy of locating a wireless node, decreases as the detected signalstrength decreases. As discussed above, the RADAR, and other RFfingerprinting, systems estimate the location of a wireless node byfinding the best fit in the Radio Maps, treating the signal strengthmeasurements associated with the different access points equally. Asdiscussed below, however, the error injected by errors in signalstrength detection can inject large amounts of error in computing thelocation of a wireless node, especially where the detected signal, atthe radio receiver, is weak. Still further, while the RADAR systemallows a mobile station to track its own location, it does not disclosea system that allows the WLAN infrastructure to track the location ofwireless nodes, such as rogue access points. Such a system is desirableas it obviates the need for special client software to be installed onthe mobile stations.

Moreover, individual differences as to how two different wireless nodesdetect and report signal strength can cause errors in location, sincethe Radio Maps assume no error in such measurements. Accordingly, twowireless nodes in the same location that detect different signalstrengths will compute different estimated locations. Still further,while the RADAR system allows a mobile station to track its ownlocation, it does not disclose a system that allows the WLANinfrastructure to track the location of wireless nodes, such as rogueaccess points. Such a system is desirable as it obviates the need forspecial client software to be installed on the mobile stations.

This paradigm shift, however, presents certain problems. As discussedabove, the Radio Maps in the RADAR system are constructed from the pointof view of a wireless node in an RF environment that includes accesspoints in known locations. In other words, the Radio Maps areconstructed based on heuristic and/or mathematical evaluations of thepropagation of signals from the access points to a wireless node at agiven location. Accordingly, the RADAR system need not assume symmetryof path loss between a given location and the access points in the RADARsystem, since the mobile station detects the signal strength of theaccess points and computes its own location. In addition, since thelocation of a wireless node is based on path loss, the transmit power ofthe radio transmitters used to determine location must also be known. Inthe RADAR system, this is not problematic, since the signals used todetermine location are transmitted by access points, whose transmitpower can be controlled or easily determined. Estimating location basedon signals transmitted by a wireless node, however, can be problematic,since transmit power can vary among wireless device manufacturers,and/or may be individually configured by the mobile user.

One approach to this problem is to assume symmetry in path loss betweena given location in an RF environment and the radio transceivers used todetect signals transmitted by the wireless nodes. Furthermore, theseapproaches also assume a uniform transmit power for the wireless nodesin light of the fact that legal regulations, as well as current chip settechnology, generally places an upper limit on transmit power. These twoassumptions, however, can significantly impact the accuracy of locatinga wireless node. As discussed above, the RADAR system, for example,finds the location coordinates in the Radio Map that are the best fitbased on the detected signal strengths. That is, for each point in theRadio Map, the location metric computes the Euclidean distance betweenthe detected signal strength values and the values in the Radio Map.

The following equation provides an illustrative example, assume fordidactic purposes that a given wireless node is detected by three accesspoints. The signal strength samples are RSSIap1 RSSIap2, and RSSIap3,while the RF coverage maps for each of the access points are denoted asMAPap1, MAPap2, MAPap3, where the coverage maps include access pointsignal strength values detected or computed for different locations in adefined region. Further assume that all coverage maps have values at alllocations within the search region. Again, assuming path loss symmetryand a uniform transmit power, individual error surfaces for each accesspoint can be created based on the signal strength detected at eachaccess point, (RSSIap1, etc.) and the signal strength values in theindividual coverage maps (e.g., MAPap1, etc.). That is, the errorsurface is the difference between the observed signal strength at agiven access point less the signal strength values in the coverage map.The locations in this coverage map where the difference is zero aregenerally the likely or estimated locations relative to each radioreceiver. In many situations, however, the measured signal strengths,RSSIap1 RSSIap2, and RSSIap3, do not match the signal strengths recordedin the coverage maps MAPap1, MAPap2, MAPap3 at any one location. In thiscase, it is desirable to find the location that is “closest” to matchingRSSIap1 RSSIap2, and RSSIap3—in other words, the location that minimizessome function of MAPap1, MAPap2, MAPap3, RSSIap1 RSSIap2, and RSSIap3.Bahl et al., supra, describe several ways in which this function iscreated, including minimum mean squared error, minimum distance, andminimum Manhattan grid distance.

Furthermore, a total error surface, ErrSurf, can be computed based onthe sum of the squares (to neutralize positive and negative differences)of the individual error surfaces (i.e., the difference between thedetected signal strength values and the signal strength values in eachcoverage maps), as follows:ErrSurf=[(RSSIap1−MAPap1)²+(RSSIap2MAPap2)²+(RSSIap3−MAPap3)²]/3. In oneimplementation, the estimated wireless node location is derived from theminimum or minimum of this total error surface.

However, a change in the wireless node's transmit power (or, in theRADAR system, inaccuracies in detecting signal strength by the wirelessnodes) will adversely affect the accuracy of this metric. For example, aN dB difference between the actual and assumed transmit power of awireless node would cause a N dB change in the detected signalstrengths. Rather than merely shifting the individual signal strengthdifferences for each point in the individual error surfaces up by somefixed amount, the individual differences between the detected signalstrengths and the signal strength values in the error surface can changequite dramatically. Indeed, each point in the individual error surfacesare shifted an amount proportional to the dB error. This circumstancemoves some areas of the total error surface up relative to others, andsome areas of the total error surface down relative to others,significantly altering the shape of the error surface, as well as thelocation, shape, and size of its minima. It also creates unpredictableerror with changes in transmit power. Similar problems will occur for afixed error in the “link or path loss symmetry” where the path loss fromaccess point to wireless node differs from the path loss from wirelessnode to access point by some fixed amount due to propagationcharacteristics, vantage point, and the like. In addition, sources of RFinterference typically have unknown transmit powers, and may onlypartially overlap the frequency band in which wireless nodes operate.Estimating the location of these interference sources requires a methodthat does not depend entirely on the absolute detected signal strengthvalue.

In light of the foregoing, a need in the art exists for a wireless nodelocation mechanism that reduces the errors in computing the location ofa wireless node due to errors in signal strength detection. A need inthe art also exists for a wireless node location mechanism that reducesthe errors in computing the location of a wireless node due to commonlyoccurring circumstances, such as variations in wireless node transmitpower, errors in signal strength detection, and/or direction-dependentpath loss. Embodiments of the present invention substantially fulfillthese needs.

SUMMARY OF THE INVENTION

The present invention provides methods, apparatuses, and systemsdirected to a wireless node location mechanism that uses a signalstrength weighting metric to improve the accuracy of estimating thelocation of a wireless node based on signals detected among a pluralityof radio transceivers. In certain implementations, the wireless nodelocation mechanism further incorporates a differential signal strengthmetric to reduce the errors caused by variations in wireless nodetransmit power, errors in signal strength detection, and/ordirection-dependent path loss. As opposed to using the absolute signalstrength or power of an RF signal transmitted by a wireless node,implementations of the present invention compare the differences betweensignal strength values detected at various pairs of radio receivers tocorresponding differences characterized in a model of the RFenvironment. One implementation of the invention searches for thelocations in the model between each pair of radio receivers where theirsignal strength is different by an observed amount. As discussed in moredetail below, the wireless node location mechanism can be incorporatedinto wireless network environments, such as 802.11 networks, to estimatethe location of mobile stations, rogue access points and other wirelessnodes.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram including a wireless node locationmechanism according to an implementation of the present invention.

FIG. 2 is a flow chart diagram illustrating the overall process flowdirected to the location of a wireless node according to animplementation of the present invention.

FIG. 3 is a functional block diagram illustrating a wireless networksystem according to an implementation of the present invention.

FIG. 4 is a functional block diagram showing the wireless node locationfunctionality of a central control element in the wireless networksystem of FIG. 3.

FIG. 5 is a flow chart diagram illustrating the overall process flowdirected to estimating the location of a wireless node that incorporatesa weighted, differential signal strength metric.

DESCRIPTION OF PREFERRED EMBODIMENT(S)

A. Wireless Node Location and Signal Strength Weighting Metric

FIG. 1 illustrates the basic operating components of the wireless nodelocation mechanism according to an implementation of the presentinvention. As FIG. 1 shows, the wireless node location mechanismincludes a wireless node location module 59 and a plurality ofinfrastructure radio transceivers 58 disposed throughout a physicalspace. One skilled in the art will recognize that the system depicted inFIG. 1 represents an example of the basic components of the inventionand is mostly for didactic purposes. As discussed more fully below, thefunctionality generally denoted by infrastructure radio transceivers 58and wireless node location module 59 can be integrated into a variety ofsystems, such as wireless systems dedicated for location of wirelessnodes, or WLAN or other wireless network systems.

Infrastructure radio transceivers 58 generally comprise at least oneantenna, a radio transmit/receive unit, and control logic (e.g., a802.11 control unit) to control the transmission and reception of radiosignals according to a wireless communications protocol. Infrastructureradio transceivers 58, in one implementation, are disposed in knownand/or fixed locations throughout a physical space, such as a room, acollection of rooms, a floor of a building, an entire building, or anarbitrarily-defined region, including outside environments, over whichinfrastructure radio transceivers 58 provide RF coverage.

A. 1. Infrastructure Radio Transceiver

Infrastructure radio transceivers 58 are operative to detect thestrength of received radio-frequency signals, such as the signals 57transmitted by wireless node 56 and by other radio transceivers, andprovide the detected signal strength data for corresponding wirelessnodes to wireless node location module 59. In one implementation,infrastructure radio transceivers 58 are also operative to transmit andreceive wireless or radio-frequency signals according to a wirelesscommunications protocol, such as the IEEE 802.11 WLAN protocol.Infrastructure radio transceivers 58, in one implementation, can operateon a selected channel from a plurality of channels in a given band. Inanother implementation, infrastructure radio transceivers 58 can alsooperate in more than one band. For example, infrastructure radioreceivers 58 may be configured to operate in either the 802.11a-5 GHzband, and/or the 802.11b/g-2.4 GHz band. In one implementation,infrastructure radio transceivers 58 can be configured to collect thesignal strength information associated with wireless nodes and transmitthe collected data in response to SNMP or other requests by wirelessnode location module 59. In other implementations, the infrastructureradio transceivers 58 can transmit signal strength information on aregular or periodic basis. As discussed below, other methods forcollecting signal strength data may also be employed.

Identification of wireless nodes depends on the wireless communicationsprotocol in use. For 802.11 WLAN environments, for example, wirelessnodes can be identified based on MAC address. Furthermore, wirelessnodes can be authorized mobile stations, such as remote client elements16, 18 (see FIG. 3), rogue systems (e.g., rogue access points and/orrogue mobile stations), as well as authorized access points for which nolocation information is known. In other implementations, wireless nodescan be identified based on a unique property of the RF signal, such as agiven frequency channel, or a unique signal pattern, and the like. Forexample, the wireless node location functionality may be employed tolocate a detected source of interference, such as a non-802.11 compliantdevice.

In one implementation, infrastructure radio transceivers 58 are alsooperable to communicate with one or more mobile stations, such aswireless node 56, according to a wireless communication protocol. Forexample, radio transceiver 58, in one implementation, is an access pointor other WLAN component. In one implementation, radio transceiver 58 isoperably connected to a Local Area Network (LAN), Wide Area Network(WAN) or other wireline network to bridge traffic between mobilestations and the wireline network. As discussed more fully below, radiotransceiver 58 may also be an access element or light weight accesspoint in a wireless network featuring hierarchical processing ofprotocol information. In one implementation, the radio transceiver 58implements the 802.11 protocols (where 802.11, as used herein,generically refers to the IEEE 802.11 standard for wireless LANs and allits amendments). Of course, the present invention can be used inconnection with any suitable radio-frequency-based wireless network orcommunications protocol.

In one implementation, infrastructure radio transceivers 58 make use ofthe signal strength detection functionality residing on a wirelessnetwork interface adapter to detect signal strength on a frame-by-framebasis. For example, the IEEE 802.11 standard defines a mechanism bywhich RF energy is measured by the circuitry (e.g., chip set) on awireless network interface controller. The IEEE 802.11 protocolspecifies an optional parameter, the receive signal strength indicator(RSSI). This parameter is a measure by the PHY layer of the energyobserved at the antenna used to receive the current packet or frame.RSSI is measured between the beginning of the start frame delimiter(SFD) and the end of the PLCP header error check (HEC). This numericvalue is an integer with an allowable range of 0–255 (a 1-byte value).Typically, 802.11 chip set vendors have chosen not to actually measure256 different signal levels. Accordingly, each vendor's 802.11-compliantadapter has a specific maximum RSSI value (“RSSI_Max”). Therefore, theRF energy level reported by a particular vendor's wireless networkadapter will range between 0 and RSSI_Max. Resolving a given RSSI valuereported by a given vendor's chip set to an actual power value (dBm) canbe accomplished by reference to a conversion table. In addition, somewireless networking chip sets actually report received signal strengthin dBm units, rather than or in addition to RSSI. Other attributes ofthe signal can also be used in combination with received signal strengthor as an alternative. For example, the detected Signal-to-Noise Ratio(SNR) during packet reception can be used in determining overlay signaltransmit power. Again, many chip sets include functionality andcorresponding APIs to allow for a determination of SNRs associated withpackets received from other transceivers 58 and/or wireless node 56. Theresulting signal strength information, in one implementation, can beassociated with a time stamp corresponding to the receipt of the frame.As discussed herein, this signal strength information can be collectedat each infrastructure radio transceiver 58 and/or the wireless nodelocation module 59 in suitable data structures.

A.2. Wireless Node Location Module

Wireless node location module 59, in one implementation, collects signalstrength data received from infrastructure radio transceivers 58 andmaintains the signal strength data in association with a wireless nodeidentifier, and an identifier for the infrastructure radio transceiver58 which provided the signal strength data. Wireless node locationmodule 59, in one implementation, is also configured to distinguishbetween signals received from infrastructure radio transceivers 58 andsignals received from other wireless nodes based on the wireless nodeidentifier. In one implementation, wireless node location module 59maintains a variety of data structures for storing signal strengthinformation. For example, one data structure is used to store the signalstrength of signals transmitted between infrastructure radiotransceivers 58. In one implementation, wireless node location module 59stores this signal strength data in a N×N IRT matrix, where N is thenumber of infrastructure radio transceivers 58. The column entries cancorrespond to the transmitting transceiver, while the row entriescorrespond to the receiving transceiver, or vice versa. Various entriesin this matrix may be null values as all infrastructure radiotransceivers may not, and in most deployments probably will not, be ableto detect one another. Wireless node location module 59, in oneimplementation, maintains signal strength data for all other wirelessnodes in tables or other suitable data structures. In oneimplementation, wireless node location module 59 maintains, for eachradio transceiver 58, a separate table including at least two fields: 1)a wireless node identifier; and 2) the detected signal strength.Additional fields may also include a time stamp indicating the time theinfrastructure radio transceiver 58 received the signal. In oneimplementation, when the memory space allocated to the wireless nodetables is depleted, the least recently used/updated entry as indicatedby the time stamps is overwritten. In one implementation, wireless nodelocation module 59 filters the signal strength data received from theinfrastructure radio transceivers 58 against a list of wireless nodeidentifiers in order to identify the appropriate data structure toupdate. One skilled in the art will recognize that a variety of datastructures beyond matrixes and tables can be used.

As discussed above, signal strengths are detected, in oneimplementation, on a frame-by-frame basis. Accordingly, in oneembodiment, the signal strength data maintained by wireless nodelocation module 59 can be updated as the frames/packets are received. Inone implementation, the latest signal strength value is used toessentially overwrite the old value. In other implementations, however,an average, moving average or weighted moving average can be used ifsuccessive wireless frames corresponding to a given wireless node areencountered within a threshold time interval (e.g., typically resultingfrom a data stream transmission). In such a situation, the time stampcan correspond to the time of the last packet or frame. In addition,while radio transceivers 58 when operating as access points typicallyoperate on different channels, mobile stations at various times (e.g.,transmitting probe requests to find access points) transmit wirelessframes on all available operating channels. This helps to ensure that aplurality of infrastructure radio transceivers 58 detect the mobilestation. In some implementations, one or more infrastructure radiotransceivers 58 that are adjacent to a radio transceiver 58 thatdetected a given wireless node may be directed to switch to a givenoperating channel to listen for signals transmitted by the mobilestation. Still further, as discussed below, the infrastructure radiotransceivers 58 may be commanded to specifically transmit frames on agiven channel for the purpose of updating the signal strength datamaintained by wireless node location module 59.

Wireless node location module 59 also maintains a RF physical model ofthe coverage area associated with the RF environment. As discussed inmore detail below, the RF physical model returns an estimated physicallocation of a wireless node, given the strength of signals detected bythe infrastructure radio transceivers 58, as well as an indication ofthe infrastructure radio transceivers reporting the signal strengths. Inone implementation, the RF physical model characterizes for eachinfrastructure radio transceiver 58 the received signal strengthassociated with a wireless transmitter at a given location. For example,in one implementation, the RF physical model comprises, for eachantenna, a radio coverage map or matrix that indicates the expectedsignal strength detected at an infrastructure radio transceiver receivedfrom a wireless node, assuming a uniform transmit power, at a givenlocation defined in x-, and y-coordinates. This database can bepopulated in a variety of ways. For example, the radio coverage maps canbe populated with the results of an extensive site survey, according towhich a wireless transmitter is placed at different locations in thephysical space. During the site survey, the infrastructure radiotransceivers 58 operate in a listening mode that cycles between theantennas and report the resulting signal strength of the signaltransmitted by the wireless node used to conduct the site survey. In oneimplementation, the infrastructure radio transceivers 58 can beconfigured to transmit the signal strength data back to the wirelesstransmitter, which may be a laptop computer or other wireless device.The coverage maps are constructed by associating the signal strength andlocation data in the coverage maps corresponding to each infrastructureradio transceiver. The coverage maps may also be constructed by having aWLAN tester (or other wireless node) simply measure the signal strengthof frames transmitted by the infrastructure radio transceivers 58 (e.g.,beacon packets) at desired locations within the deployment region. Ifpath loss symmetry is assumed, these values can be used to construct thecoverage maps for each of the infrastructure radio transceivers. Toestimate the location of the wireless node, wireless node locationmodule 59 determines the location coordinates, or range of locationcoordinates, that best fit the coverage maps associated with theinfrastructure radio transceivers 58 selected to locate the wirelessnode based on the detected signal strength data, as discussed in moredetail below.

In one implementation, a coverage map, for each infrastructure radiotransceiver 58, is maintained that includes the signal strengths in anN×M matrix, where N is the number of x-coordinates in the coverage map,and M is the number of y-coordinates in the coverage map. In oneimplementation, the extent of the physical space model by the coveragemaps for each infrastructure radio transceiver 58 are co-extensive. Thecoverage maps for all infrastructure radio transceivers 58 can beco-extensive with the physical space in which the location system isdeployed, or with a boundary configured by a network administrator. Inone implementation, however, knowledge of various antenna attributesassociated with each infrastructure radio transceiver 58—such as antennatype (e.g., omni-directional, directional), peak gain orientation,beamwidth, front-to-back isolation—can be used to compress or reduce thesize of the coverage maps. In one implementation, the coverage maps canbe configured to be substantially coextensive with the antenna patternof each antenna connected to the infrastructure radio transceivers 58out to a threshold signal strength or gain level. For example, thecoverage map for a given antenna can be compressed to the front orintended coverage area of the directional antenna. Of course, other datastructures can be used such as a table including location coordinatesstored in association with tuples of signal strengths and infrastructureradio transceiver antenna identifiers. In addition, if the coverage mapsare compressed, the search for the best fit across selected coveragemaps can be isolated to the overlap between coverage maps associatedwith the antennas selected to locate the wireless node.

In another implementation, the coverage maps of the RF physical modelmay be constructed using RF prediction to model the coverage area,employing mathematical techniques like ray-tracing, and the like. In oneimplementation, the RF prediction model can be computed for eachcoordinate location in a desired physical space, assuming a uniformwireless node transmit power. The estimated signal strength informationfor each infrastructure radio transceiver 58 can be used to populate thecoverage maps discussed above. In an alternative embodiment, RFprediction models can be computed relative to each infrastructure radiotransceiver antenna. If path loss symmetry and transmit power symmetrybetween the wireless nodes and the infrastructure radio transceivers 58are assumed, the coverage maps for each infrastructure radio transceiverantenna can be populated by using the computed values at each of thecoordinate locations in the coverage map. Of course, site survey datacan also be used to adjust one or more parameters associated with the RFprediction model used to estimate expected signal strength at thevarious locations. As above, the boundaries of the coverage maps can becontoured based on the properties of the antennas connected to theinfrastructure radio transceivers 58. In addition, the locationcoordinates in the coverage maps can be two-dimensional, x- andy-coordinates, defining location in a horizontal plane. The locationcoordinates can also be three-dimensional, x-, y- and z-coordinates.Other coordinate systems can be used, such as spherical coordinates orcylindrical coordinates. In addition, the values of the coordinates canbe either global (i.e., longitude and latitude) or expressed relative toan arbitrarily-defined origin. In addition, the granularity of thecoordinates in the coverage maps depends on the desired granularity ofthe wireless node location estimates. In addition, in dual-bandconfigurations, separate coverage maps may be maintained for eachinfrastructure radio transceiver 58 for the different frequency bands(e.g., 2.4 GHz and 5 GHz).

In one implementation, the wireless node location module 59 includesmore than one RF physical model of the environment (in oneimplementation, each RF physical model is a set of coverage mapscorresponding to the antennas of the infrastructure radio transceivers58), and uses signals transmitted between the infrastructure radiotransceivers 58 to dynamically select one of the RF physical models(such as a set of coverage maps) that best characterizes the current RFenvironment. As discussed above, the propagation of RF signals iseffected by a variety of objects, including people, that move within anRF environment. In one implementation, the wireless node locationfunctionality can compare the signal strength data in the N×N IRT signalstrength matrix and the known locations of the infrastructure radiotransceivers against the RF physical models to find the best fit. In oneimplementation, infrastructure radio transceivers 58 can be configuredto transmit wireless frames at regular intervals on one to a pluralityof operating channels within a given frequency band to allow for theother infrastructure radio transceivers 58 to detect the signals. U.S.application Ser. No. 10/447,735 discloses the transmission of frames fordetection by neighboring WLAN transceivers. In another implementation,infrastructure radio transceivers 58 transmit frames, on demand, inresponse to a command issued by wireless node location module 59.

FIG. 2 illustrates a method, according to one implementation of thepresent invention, directed to estimating the location of a wirelessnode. The wireless node location functionality can be triggered ondemand, for example, in response to a command issued by a networkadministrator using a control interface to locate a mobile stationidentified by a MAC address or other suitable identifier, such as anarbitrary name associated with a MAC address in a table or other datastructure. Wireless node location module 59 may also be triggeredautomatically in response to the detection of a rogue access point. U.S.application Ser. No. 10/407,370, incorporated by reference above,discloses detection of rogue access points in a wireless network system.Wireless node location module 59 can also be configured to periodicallydetermine the location of a given mobile station in order to track itsmovement over a period of time.

A.3. Signal Strength Weighting Metric

As FIG. 2 illustrates, wireless node location module 59, in oneimplementation, begins by selecting the infrastructure radiotransceivers (IRTs) 58 whose signal measurements will be used inlocating the desired wireless node (102). In one implementation,wireless node location module 59 scans the data structures discussedabove to identify the infrastructure radio transceivers 58 that see ordetect wireless frames transmitted by the desired wireless node. Inimplementations where signal strength data is regularly collected (asopposed to on demand), the time stamps in the data structures can beused to filter out infrastructure radio transceivers 58 that have notdetected the desired wireless node within a threshold period of time.Additional or alternative filter criteria can include a threshold signalstrength level (such as −80 dBm). In the implementation shown, wirelessnode location module 59 selects the M infrastructure radio transceivers58 that report the strongest signal strengths (where M is a configurableparameter). In one implementation, if an insufficient number ofinfrastructure radio transceivers 58 are identified, wireless nodelocation module 59 can command the infrastructure radio transceivers 58to actively scan for the desired wireless node and return signalstrength information. Wireless node location module 59 collects thesignal strength (e.g., RSSI) measurements corresponding to the selectedinfrastructure radio transceivers 58 (104), and identifies the RFcoverage maps to be used in estimating the location of the wireless nodebased on selected infrastructure radio transceivers 58 (106).

As FIG. 2 shows, wireless node location module 59, for all selectedinfrastructure radio transceivers (108), computes, for each point in thecoverage map, MAP_(i), an error surface, ErrSurf_(i), characterizing thedifference between the signal strength, SS_(i), detected by theinfrastructure radio transceiver and the value in the correspondingcoverage map (110). To neutralize positive and negative errors, wirelessnode location module 59, in one implementation, uses the square of theerror for each point in the error surface. As FIG. 2 illustrates,wireless node location module 59 sums the individual error surfaces,ErrSurf_(i), to create a total error surface, TotalErrSurf, for allpoints for which the error surfaces overlap (112). However, as FIG. 2shows, the contribution of each error surface, ErrSurf_(i), is weightedby a weighing function, WF, whose value depends on the detected signalstrength, SS_(i), reported by the infrastructure radio transceiver 58.To estimate the location of the desired wireless node, wireless nodelocation module 59, in one implementation, selects the location thatminimizes the total error surface, TotalErrSurf (120). In oneimplementation, wireless node location module 59 computes the estimatedlocation by finding the location that minimizes the Euclidian distancein signal space of the Total Err Surf, which essentially minimizes theEuclidian distance in signal space between the detected signal strengthvalues and the signal strength values in the corresponding coveragemaps.

The weighting function, WF, in one implementation, expresses theconfidence in the individual error surface (ErrSurf_(i)) locationrelative to error in distance caused by potential errors in signalstrength detection. In the implementation shown, confidence in the errorsurface (ErrSurf_(i)) is defined by how much distance error would becaused by a 1 dB change in the signal strength measurement, SS_(i), fromthe infrastructure radio transceiver 58. This confidence weightingfunction could vary depending on topology (e.g., the presence andlocation of interior walls, dividers, doors, etc.) and path lossexponent. However, in one implementation, it is assumed that this errorin unit distance per dB error is a function of only detected signalstrength and the path loss exponent. According to this implementation,the error in unit distance per dB is a geometric or exponentialfunction. The weights for each error surface, therefore, are exponentialwith higher detected signal strengths resulting in higher weightingvalues. For a single DB change the error, er; in unit distance (e.g.,feet, meters, etc.) changes by:er=10^(1/10*PL)

So, for example, if the path loss exponent, PL, is 2.4, the error, er,is 1.1007. Accordingly, for a 1 dB error at 100 feet, for example, wouldcause 10.07 feet of error in the estimated location in estimateddistance, while a 1 dB error at 1000 feet would cause 100.7 feet oferror.

Given the error values discussed above, the weighting function, WF, inone implementation, is as follows:WF(SS _(i)) equals 1, if SS _(i) >Th; else, equals er ^((SSi−Th)).

As the weighting function equation set forth above, demonstrates, WFequals one for signal strength measurements above a threshold, Th (suchas −50 dBm). Furthermore, as the weighting function above demonstrates,the weighting function decreases with decreasing signal strengthmeasurements as lower measurements indicate increased distance betweenthe transmitting radio and receiving radio. Moreover, as one skilled inthe art will recognize the weights will change with respect to path lossexponent, frequency (2.4 v 5.1 GHz), and threshold (Th). Still further,in certain dual-band implementations, where signal strength informationis collected in more than one frequency band, the signal strengthweighting metric may weight samples from one band (e.g., 2.4 GHz) higherthan samples obtained in another band (e.g., 5 GHz). The difference inweighting between bands can be based on an empirical study of thelocation error employing each separate band.

A.4. Signal Strength Weighting Metric and Differential Metric

FIG. 5 illustrates an alternative implementation of the presentinvention in which the signal strength weighting metric is incorporatedinto an algorithm that estimates the location of wireless nodes based onthe differences in reported signal strength across selectedinfrastructure radio transceivers 58. As FIG. 5 illustrates, wirelessnode location module 59, in one implementation, begins by selecting theinfrastructure radio transceivers (IRTs) 58 whose signal measurementswill be used in locating the desired wireless node (102). In addition,wireless node location module 59 collects signal strength measurements(104) and selects RF coverage maps (106) in a manner similar to thatdescribed in connection with FIG. 2.

As FIG. 5 illustrates, wireless node location module 59, computes thedifference, ΔSS_(ij), for each pair of signal strength measurements(SS_(i) and SS_(j)) among the selected infrastructure radio transceivers58 (212), as well as the difference, ΔMAP_(ij), at each point betweenthe coverage maps (MAP_(i) and MAP_(j)) corresponding to the selectedinfrastructure radio transceivers 58 (214). In the case of M selectedinfrastructure radio transceivers 58, there are N choose 2 orN!/((N−2)!2!) pairs of differences. Wireless node location module 59constructs a total difference error surface, ErrSurfDiff, by computing,for each unique pair of infrastructure radio transceivers 58 (see 208,210), the square of the difference between ΔSS_(ij) and ΔMAP_(ij), andadding the contribution from each unique pair to ErrSurfDiff (216 a, 216b). Wireless node location module 59, however, weights each contributionby the weighting function, WF. As FIG. 5 shows, each contribution isweighted using the lesser value of the signal strength measurements,SS_(i) and SS_(j), for each unique pair of infrastructure radiotransceivers 58 (215), since the potential error attributable to thelesser signal strength value generally dominates over the errorattributable to the higher of the signal strength values in the pair.Lastly, to estimate the location of the desired wireless node, wirelessnode location module 59 selects the location that minimizes the totaldifference error surface, ErrSurfDiff (220).

As the foregoing illustrates, wireless node location module 59, in thisimplementation, essentially searches for the area between each pair ofinfrastructure radio transceivers 58 where the detected signal strengthis different by X dB, where X dB is the observed difference in the RSSIor other signal strength measurements of the desired wireless node asdetected by the two infrastructure radio transceivers. In the ideal caseinvolving no physical barriers are located in the RF environment (andassuming the use of omni-directional antennas or antenna patternsapproximating omni-directional antenna patterns), the contour where thedifference between the two infrastructure radio transceivers 58predicted signals is a constant X dB can be described by a Cartesianoval. In the practical world, this shape is arbitrary. In the idealworld, summing up several of these create a region of zero error wherethe Cartesian ovals overlap and non-zero error terms elsewhere. In thereal world, the summing up of several of these surfaces create a totalerror surface, whose minimums represent the estimated location of thewireless node.

To illustrate the benefits of this differential signal strength metric,assume a N dB difference in the transmit power of the wireless node froman assumed or default transmit power. In the case of three selectedinfrastructure radio transceivers, this N dB difference factors into thetotal difference error surface as:ErrSurfDiffTxErr=(((RSSIap1+N)−(RSSIap2+N))−(MAPap1−MAPap2))^2+(((RSSIap1+N)−(RSSIap3+N))−(MAPap1−MAPap3))^2+(((RSSIap2+N)−(RSSIap3+N))−(MAPap2−MAPap3))^2,

-   -   which reduces to the original ErrSurfDiff with no additional        errors added. In addition, the differential signal strength        metric also addresses the errors in detecting the strength of        wireless signals and errors resulting from assuming path loss        symmetry. For example, the absolute power assumed by the        coverage maps (e.g., MAPap1, MAPap2) may be incorrect by K dB        for each client independently. In this metric, those errors are        also removed, as shown in the following:        ErrSurfDiffAPErr=((RSSIap1−RSSIap2)−((MAPap1+K)−        (MAPap2+K)))^2+((RSSIap1−RSSIap3)−((MAPap1+K)−(MAPap3+K)))^2+((RSSIap2−RSSIap3)−((MAPap2+K)−(MAPap3+K)))^2,    -   which also reduces to ErrSurfDiff. Accordingly, the differential        signal strength metric described herein minimizes the effects of        absolute error caused by variations in transmit power, or signal        strength detection, as well as lack of symmetry in path loss        between a detecting node and a transmitting node.        B. Integration into Wireless Network Systems

In one implementation, the wireless node location functionalitydiscussed above can be integrated into a wireless networkinfrastructure, such as the hierarchical WLAN system illustrated in FIG.3. For example, the wireless node location functionality describedherein may be integrated into a WLAN environment as disclosed in U.S.application Ser. Nos. 10/155,938 and 10/407,357 incorporated byreference herein. The wireless node location functionality according tothe present invention, however, may be applied to other wireless networkarchitectures. For example, the wireless node location functionality maybe integrated into a wireless network infrastructure including aplurality of substantially autonomous access points that operate inconnection with a central network management system.

Referring to FIG. 3, there is shown a block diagram of a wireless LocalArea Network system according to an embodiment of the invention. Aspecific embodiment of the invention includes the following elements:access elements 11–15 for wireless communication with selected clientremote elements 16, 18, 20, 22, central control elements 24, 25, 26, andmeans for communication between the access elements and the centralcontrol elements, such as direct line access, an Ethernet network, suchas LAN segment 10. As disclosed in U.S. patent application Ser. No.10/407,357, the access elements, such as access elements 11–15 aredirectly connected to LAN segment 10 or a virtual local area network(VLAN) for communication with a corresponding central control element24, 26. See FIG. 3. As disclosed in U.S. patent application Ser. No.10/155,938, however, access elements 11–15 may also be directlyconnected to respective central control elements 24, 26 via directaccess lines.

The access elements 11–15 are coupled via communication means using awireless local area network (WLAN) protocol (e.g., IEEE 802.11a or802.11b, etc.) to the client remote elements 16, 18, 20, 22. Asdescribed in U.S. application Ser. Nos. 10/155,938 and 10/407,357, theaccess elements 12, 14 and the central control element 24 tunnel networktraffic associated with corresponding remote client elements 16, 18; 20,22 via direct access lines or a LAN segment 10. Central control elements24, 26 are also operative to bridge the network traffic between theremote client elements 16, 18; 20, 22 transmitted through the tunnelwith corresponding access elements 11–15. In another implementation,access elements 11–15 may be configured to bridge the network traffic onLAN segments 10, while sending copies of the bridged frames to theaccess elements for data gathering and network management purposes.

As described in the above-identified patent applications, centralcontrol elements 24, 26 operate to perform data link layer managementfunctions, such as authentication and association on behalf of accesselements 11–15. For example, the central control elements 24, 26 provideprocessing to dynamically configure a wireless Local Area Network of asystem according to the invention while the access elements 11–15provide the acknowledgment of communications with the client remoteelements 16, 18, 20, 22. The central control elements 24, 26 may forexample process the wireless LAN management messages passed on from theclient remote elements 16, 18; 20, 22 via the access elements 11–15,such as authentication requests and authorization requests, whereas theaccess elements 11–15 provide immediate acknowledgment of thecommunication of those messages without conventional processing thereof.Similarly, the central control elements 24, 26 may for example processphysical layer information. Still further, the central control elements24, 26, as discussed more fully below, may for example processinformation collected at the access elements 11–15 on channelcharacteristics, signal strength, propagation, and interference ornoise.

Central control elements 24, 26, as shown in FIG. 4, may be configuredto gather the signal strength data discussed above to support thewireless node location functionality according to the present invention.The signal strength data gathering functionality described herein isquite similar to the data gathering disclosed in U.S. application Ser.No. 10/183,704, incorporated by reference above. In that application,access elements 11–15 append signal strength data to packets receivedfrom wireless nodes, typically, in encapsulating headers. The centralcontrol elements 24, 26 process the encapsulating packet headers toupdate various data structures, such as the N×N AP signal strengthmatrix and wireless node tables discussed above in Section A. U.S.application Ser. No. 10/183,704 discloses the internal operatingcomponents and general configuration of access elements 11–15 that canbe used in connection with the integrated wireless node locationfunctionality described herein.

FIG. 4 illustrates the logical configuration of central control elements24, 26, according to an implementation of the present invention. Asdiscussed in U.S. application Ser. No. 10/183,704, in oneimplementation, there is both a logical data path 66 and a control path68 between a central control element 24 or 26 and an access element(e.g., access element 11). The control path 68 allows the centralcontrol element 24 or 26 to communicate with the radio access elements11–15 and acquire the signal strength between the radio access elements.By monitoring the data path 66, the central control element 24, 26 canobtain the signal strength of the signals transmitted by other wirelessnodes.

More specifically, the wireless node locator 90 in the central controlelement 24 or 26 collects information from a plurality of accesselements via a control channel 68 and a data channel 66. The centralcontrol element 24 or 26 receives and transmits data packets and controlpackets from/to a plurality of access elements 11–15 as described above.A flag detector 62 distinguishes between data packets and controlpackets, routing them through a logical switch 64 to a high-speed datapath 66 in communication with the wired network 15 or to control path 68within the central control element 24 or 26. The data path 66 ismonitored by a wireless node data collector 70. Associated with eachdata packet is a resource management header which contains RF physicallayer information, such as the power in the channel before each receivedpacket, an identifier for the access element receiving the signal, aswell as an identifier for the antenna selected to receive the signal.This information, together with the 802.11 protocol information in thenative frames, can be used to maintain one or more data structures thatmaintain signal strength data for the wireless nodes detected by theaccess elements 11–15, as discussed in section A, above. The controlpath 68 is coupled to a processor element 76 in which an AP signalstrength matrix 78 is maintained. The AP signal strength matrix 78collects information quantifying the signal strength between accesselements 11–15. All of the signal strength data are collected at theaccess elements 11–15 and communicated over the data path and controlpath to the central control element 24 or 26, in one implementation, aspacketized information in the resource management header in the datapath and resource management control packets in the control path,respectively.

As discussed above, in one implementation, the wireless node locationfunction uses signal strength data between access elements to select aRF physical model that best characterizes the current RF environment. Tosupport such an implementation, one task is to create and maintain an APsignal strength matrix for all the remote access elements in the variouswireless networks which detect each other's signals. This isaccomplished, in one implementation, by having the wireless node locator90 in the central control element 24 or 26 and a Resource Manager in theaccess elements 11–15 both passively listen to surrounding accesselements and actively probe for surrounding access elements. Thewireless node locator in the central control element 24 or 26 canschedule an access element 11–15 in the wireless network to transmit adata measurement request on a specified channel and then recordresponses from surrounding access elements. The data measurement proberequest and the receiver information bandwidth can have a narrowerinformation bandwidth than the normal information bandwidth in order toallow the dynamic range of the receiver to be extended beyond its normaloperational range. This allows a radio element to “see” access elementsbeyond its normal operating range. Scheduling these measurements allowsmultiple measurements to be made with a single transmission and allowsthe detection of the transmitting signal to be recognized as a change inamplitude relative to the background noise at the scheduled time,allowing for easier detection of the measurement signal and greaterdynamic range. The resulting data can be transmitted in control packetscollected by AP signal strength matrix 78 on the control path 68.Passively, for each packet received on the data channel at the accesselement a measurement of the power in the RF channel is made immediatelybefore the received packet. This interference measurement is sent to thecentral control element via the data channel by appending a RadioResource Manager header to the data packet. Alternatively, the accesselements may be configured to flag packets received from other accesselements such that they are transmitted on the control path 68. The APsignal strength data can be used to select from different coverage mapsthat best characterize the current RF environment, as discussed above.The AP signal strength data can also be used to dynamically compute oradjust the path loss exponent(s) used in the weighting functiondescribed above. For example, since the distance between two or moreaccess elements are known, path loss exponent(s) may be computed basedon the observed signal attenuation between pairs of access elements.

FIG. 4 also illustrates an RF physical model database 80 containing theone or more coverage maps associated with the access elements 11–15.When activated, the wireless node locator 90 can operate as discussedabove to compute the estimated location of a desired wireless node, andreturn the estimated location to the requesting system, such as anetwork management system or a control interface. In the WLAN systemdepicted in FIG. 3, several implementations are possible. For example,central control element 24 may be configured as a “master” centralcontrol element for purposes of wireless node location. That is, datacollected at all central control elements is ultimately transmitted(either regularly or on demand) from other central control elements(e.g., central control element 26) to the master central control element24 which computes the estimated location. Alternatively, the collecteddata can be transmitted to a network management system that performs thelocation computations discussed above. Alternatively, central controlelements 24, 26 (when deployed in separate physical spaces, such asseparate floors or buildings) may operate substantially autonomously.

The invention has been explained with reference to specific embodiments.For example, although the embodiments described above operate inconnection with 802.11 networks, the present invention can be used inconnection with any wireless network environment. In addition, althoughthe embodiments described above operate in connection with a RF physicalmodel including a plurality of coverage maps or matrixes, other datastructures can be used to store the RF physical model data. Stillfurther, although the embodiments described above compute estimatedlocation based on signal strengths detected by infrastructure accesspoints or elements, the signal strength weighting metric can be used byclient wireless nodes, such as mobile stations in the RADAR system. Inaddition, while the location algorithms discussed above consider thesquare of the Euclidean distance between the RSSI vector and the MAPvector at a particular location, other minimization functions can beconsidered, including minimum mean squared error, minimum distance, andminimum Manhattan grid distance. Other embodiments will be evident tothose of ordinary skill in the art. It is therefore not intended thatthe invention be limited except as indicated by the appended claims.

1. A method for estimating the location of a wireless node relative to aplurality of radio receivers operative to detect the strength of RFsignals, wherein a RF coverage map, corresponding to each of the radioreceivers, characterizes the signal strength values for locations in aphysical region, comprising collecting signal strength values, detectedat a plurality of radio receivers, corresponding to signals transmittedby a wireless node, wherein at least one of the collected signals istransmitted by the wireless node in a first frequency band, and whereinat least one other of the collected signals is transmitted by thewireless node in a second frequency band; computing the estimatedlocation of the wireless node based on the collected signal strengthvalues and the RF coverage maps corresponding to the plurality of radioreceivers, wherein the contribution of each detected signal strengthvalue to the estimated location is weighted according to a weightingfunction that varies with the signal strength values detected by theradio receivers, wherein the weighting function weights the signalstrength values associated with the first frequency band higher than thesignal strength values associated with the second frequency band.
 2. Themethod of claim 1 wherein the computing step comprises computing, foreach radio receiver, an individual error surface based on the RFcoverage map associated with the radio receiver and the signal strengthdetected by the radio receiver; weighting each of the individual errorsurfaces according to a weighting function that varies with the signalstrength detected by corresponding radio receivers; aggregating theindividual error surfaces to create a total error surface; and findingthe location of the minimum of the total error surface.
 3. The method ofclaim 2 wherein each individual error surface comprises the sum of thesquares of the difference between the signal strength values detected bya radio receiver and the signal strength values in a corresponding RFcoverage map.
 4. The method of claim 1 wherein the weighting functionexpresses the confidence in the individual error surface locationrelative to the distance error caused by potential errors associatedwith the signal strength detected by a radio receiver.
 5. The method ofclaim 1 wherein the weighting function is configured such thatcontributions associated with detected signal strengths above apredetermined threshold value are equally weighted.
 6. The method ofclaim 1 wherein the weighting function is based in part on the distanceerror caused by a 1 dB change in the signal strength detected by a radioreceiver.
 7. The method of claim 1 further comprising detecting, at aplurality of radio transceivers, the strength of signals transmitted bya wireless node.
 8. The method of claim 1 wherein the RF coverage mapseach comprise a plurality of location coordinates associated withcorresponding signal strength values.
 9. The method of claim 8 whereinthe RF coverage maps are heuristically constructed.
 10. The method ofclaim 8 wherein the RF coverage maps are based on a mathematical model.11. The method of claim 1 wherein the signals transmitted by thewireless nodes are formatted according to a wireless communicationsprotocol.
 12. The method of claim 11 wherein the wireless communicationsprotocol is the IEEE 802.11 protocol.
 13. The method of claim 1 whereinonly signal strength values above a threshold signal strength value areused to compute the estimated location of the wireless node.
 14. Anapparatus facilitating the location of a wireless node in a RFenvironment, comprising a plurality of radio receivers comprising atleast one antenna, the plurality of radio receivers operative to detectthe strength of signals transmitted by wireless nodes and provide thedetected signal strengths to a wireless node location model; wherein aRF coverage map, corresponding to each of the radio receivers,characterizes the signal strength values for locations in a physicalregion wherein at least one of the detected signals is transmitted bythe wireless node in a first frequency band, and wherein at least oneother of the detected signals is transmitted by the wireless node in asecond frequency band, and a wireless node location module operative tocompute the estimated location of the wireless node based on thecollected signal strength values and the RF coverage maps correspondingto the plurality of radio receivers, wherein the contribution of eachdetected signal strength value to the estimated location is weightedaccording to a weighting function that varies with the signal strengthvalues detected by the radio receivers, wherein the weighting functionweights the collected signal strength values associated with the firstfrequency band higher than the collected signal strength valuesassociated with the second frequency band.
 15. The apparatus of claim 14wherein the wireless node location module, in computing the estimatedlocation of the wireless node, is operative to compute, for each radioreceiver, an individual error surface based on the RF coverage mapassociated with the radio receiver and the signal strength detected bythe radio receiver; weight each of the individual error surfacesaccording to a weighting function that varies with the signal strengthdetected by corresponding radio receivers; aggregate the individualerror surfaces to create a total error surface; and find the location ofthe minimum of the total error surface.
 16. The apparatus of claim 15wherein each individual error surface comprises the sum of the squaresof the difference between the signal strength values detected by a radioreceiver and the signal strength values in a corresponding RF coveragemap.
 17. The apparatus of claim 14 wherein the weighting functionexpresses the confidence in the individual error surface locationrelative to the distance error caused by potential errors associatedwith the signal strength detected by a radio receiver.
 18. The apparatusof claim 14 wherein the weighting function is configured such thatcontributions associated with detected signal strengths above apredetermined threshold value are equally weighted.
 19. The apparatus ofclaim 14 wherein the weighting function is based in part on the distanceerror caused by a 1 dB change in the signal strength detected by a radioreceiver.
 20. The apparatus of claim 14 wherein the RF coverage mapseach comprise a plurality of location coordinates associated withcorresponding signal strength values.
 21. The apparatus of claim 20wherein the RF coverage maps are heuristically constructed.
 22. Theapparatus of claim 20 wherein the RF coverage maps are based on amathematical model.
 23. The apparatus of claim 14 wherein the signalstransmitted by the wireless nodes are formatted according to a wirelesscommunications protocol.
 24. The apparatus of claim 23 wherein thewireless communications protocol is the IEEE 802.11 protocol.
 25. Amethod for estimating the location of a wireless node relative to aplurality of radio receivers operative to detect the strength of RFsignals, wherein a RF coverage map, corresponding to each of the radioreceivers, characterizes the signal strength values for locations in aphysical region, comprising collecting signal strength values, detectedat a plurality of radio receivers, corresponding to signals transmittedby a wireless node; and computing the estimated location of the wirelessnode by comparing, for all unique pairs of radio receivers, thedifferences in the signal strength values detected by the plurality ofradio receivers to corresponding differences in the signal strengthvalues in the RF coverage maps associated with the plurality of radioreceivers, wherein the comparison is weighted as a function of at leastone of the signal strength values detected by each unique pair of radioreceivers, and wherein the computing step comprises computing, for allunique pairs of radio receivers, the sum of the squares of thedifference between the signal strength values detected by a pair ofradio receivers less the difference between the signal strength valuesin the RF coverage mans associated with the pair of radio receivers;weighting each of the computed sums based on the lower of the two signalstrength values detected by the corresponding pair of radio receivers;combining the weighted sums to create a differential error surface; andfinding the minimum of the differential error surface.
 26. The method ofclaim 25 further comprising detecting, at a plurality of radiotransceivers, the strength of signals transmitted by a wireless node.27. The method of claim 25 wherein the RF coverage maps each comprise aplurality of location coordinates associated with corresponding signalstrength values.
 28. The method of claim 27 wherein the RF coverage mapsare heuristically constructed.
 29. The method of claim 25 wherein the RFcoverage maps are based on a mathematical model.
 30. The method of claim25 wherein the signals transmitted by the wireless nodes are formattedaccording to a wireless communications protocol.
 31. The method of claim30 wherein the wireless communications protocol is the IEEE 802.11protocol.
 32. A wireless node location mechanism operating inassociation with a wireless network environment comprising a pluralityof radio receivers operative to detect the signal strength of signalstransmitted by wireless nodes, wherein a RF coverage map, correspondingto each of the radio receivers, includes signal strength values forlocations in a physical region, comprising: a wireless node locationmodule operative to receive, from at least some of the plurality ofradio receivers, the detected signal strength of RF signals transmittedby a wireless node; and compute the estimated location of the wirelessnode by comparing, for all unique pairs of radio receivers, thedifferences in the signal strength values detected by the plurality ofradio receivers to corresponding differences in the signal strengthvalues in the RF coverage maps associated with the plurality of radioreceivers, wherein the comparison is weighted as a function of at leastone of the signal strength values detected by each unique pair of radioreceivers, and wherein the compute step comprises compute, for allunique pairs of radio receivers, the sum of the squares of thedifference between the signal strength values detected by a pair ofradio receivers less the difference between the signal strength valuesin the RF coverage maps associated with the pair of radio receivers;weight each of the computed sums based on the lower of the two signalstrength values detected by the corresponding pair of radio receivers;combine the weighted sums to create a differential error surface; andfind the minimum of the differential error surface.
 33. The wirelessnode location mechanism of claim 32 wherein the weighting functionexpresses the confidence in the individual error surface locationrelative to the distance error caused by potential errors associatedwith the signal strength detected by a radio receiver.
 34. The wirelessnode location mechanism of claim 32 wherein the weighting function isconfigured such that contributions associated with detected signalstrengths above a predetermined threshold value are equally weighted.35. The wireless node location mechanism of claim 32 wherein theweighting function is based in part on the distance error caused by a 1dB change in the signal strength detected by a radio receiver.
 36. Thewireless node location mechanism of claim 32 wherein the RF coveragemaps each comprise a plurality of location coordinates associated withcorresponding signal strength values.
 37. The wireless node locationmechanism of claim 36 wherein the RF coverage maps are heuristicallyconstructed.
 38. The wireless node location mechanism of claim 36wherein the RF coverage maps are based on a mathematical model.
 39. Thewireless node location mechanism of claim 32 wherein the signalstransmitted by the wireless nodes are formatted according to a wirelesscommunications protocol.
 40. The wireless node location mechanism ofclaim 39 wherein the wireless communications protocol is the IEEE 802.11protocol.
 41. The wireless node location mechanism of claim 32 furthercomprising a plurality of radio receivers operative to detect the signalstrength of signals transmitted by wireless nodes.